A scoping literature review of global dengue age-stratified seroprevalence data: estimating dengue force of infection in endemic countries

Summary Background Dengue poses a significant burden worldwide, and a more comprehensive understanding of the heterogeneity in the intensity of dengue transmission within endemic countries is necessary to evaluate the potential impact of public health interventions. Methods This scoping literature review aimed to update a previous study of dengue transmission intensity by collating global age-stratified dengue seroprevalence data published in the Medline, Embase and Web of Science databases from 2014 to 2023. These data were then utilised to calibrate catalytic models and estimate the force of infection (FOI), which is the yearly per-capita risk of infection for a typical susceptible individual. Findings We found a total of 66 new publications containing 219 age-stratified seroprevalence datasets across 30 endemic countries. Together with the previously available average FOI estimates, there are now more than 250 dengue average FOI estimates obtained from seroprevalence studies from across the world. Interpretation The results show large heterogeneities in average dengue FOI both across and within countries. These new estimates can be used to inform ongoing modelling efforts to improve our understanding of the drivers of the heterogeneity in dengue transmission globally, which in turn can help inform the optimal implementation of public health interventions. Funding UK Medical Research Council, Wellcome Trust, Community Jameel, Drugs for Neglected Disease initiative (DNDi) funded by the French Development Agency, Médecins Sans Frontières International; Swiss Agency for Development and Cooperation and UK aid.


Introduction
Dengue is a rapidly spreading mosquito-borne viral infection transmitted to humans by Aedes mosquitoes, primarily affecting tropical and sub-tropical regions. 1 With half of the world's population at risk of contracting the disease, dengue is the leading arboviral disease among humans.However, its transmission dynamics in several countries remain poorly understood. 2 Notifications of suspected or virologically confirmed dengue cases, together with information on the age and location of the reported cases, provide the necessary information to reconstruct the immunity profile (i.e., the age-dependent susceptibility to infection) of local populations.This, in turn, can be used to estimate the transmission intensity of dengue, as defined by the force of infection (FOI), which is the per-capita rate of infection for a susceptible individual.However, while routinely collected case-notification data exists for several locations in South East Asia and Latin America, case reporting in Africa is more variable and not mandatory due to the lack of point-of-care diagnostics and more limited testing capacity-with cases mainly reported in sporadic outbreaks or in individual case reports-which has hindered arbovirus surveillance across the continent. 3erological surveillance involves the collection and testing of blood samples for the presence of IgG antibodies against dengue virus using qualitative or quantitative serological assays.This approach allows for the detection of previous infections, regardless of the level of symptoms experienced during infection. 4This is particularly important for diseases such as dengue, where asymptomatic infections are very frequent 5 and antibody levels against the infecting serotype are longlived.By providing information on past exposure, serological surveillance is particularly useful for gaining insight into the historical circulation of dengue and for reconstructing the immunity profile of a population.This, in turn, can be used to inform surveillance, preparedness planning, the optimal implementation of existing and new vector control strategies, such as the release of Wolbachia-carrying mosquitoes, 6 and vaccination strategies 7,8 and to assess their potential impact.
Several different approaches have been taken to map the risk of dengue infection in the current and changing climate, 9 using multiple risk metrics including environmental, climate and habitat suitability, 2,10,11 R0 estimates 12 and occurrence data. 13While more limited in number than occurrence data, FOI estimates have the advantage of allowing us to estimate changes in the burden of infection and disease in the presence of largescale interventions, such as age-targeted vaccination campaigns in endemic settings, where accounting for pre-existing immunity is important. 14The first global map of dengue FOI was developed by Cattarino et al., 15 where machine learning was used to link 382 geolocated dengue force of infection (FOI) estimates with a set of ecological and demographic variables.Dengue FOI predictions were then imputed globally, according to underlying climatic, environmental and demographic conditions, including in places with no (serological nor case-based) dengue surveillance.Ongoing efforts aiming to validate and refine the FOI projections and burden estimates obtained by Cattarino et al. 15 require an update of the earlier literature review conducted by Imai et al., in 2014, 4 which provided the serologicallyderived FOI estimates used by Cattarino et al. 15 for the model calibration.
In this work, a literature search was conducted to collect global dengue age-stratified seroprevalence data published between 2014 and the end of 2023 to update our current understanding of dengue transmission intensity in endemic countries.As in Imai et al., 4 these data have been used to calibrate catalytic models to estimate setting-specific dengue FOI which help characterise the heterogeneity and underlying drivers of dengue transmission and will inform preparedness and response planning going forward.

Literature search
We searched Medline, Embase and Web of Science for publications reporting age-stratified dengue seroprevalence datasets published between January 2014 and October 2023 to complement the literature search previously conducted by Imai et al. 4 We used a Boolean search query with search terms: Dengue and sero* and (prevalence or seroprevalence or positiv* or seropositiv*) The specific search terms for each database are reported in the Supplementary data (Tables S1 and S2).After removing duplicate articles, articles in languages other than English, and non-article publications (e.g., conference posters and book chapters), we retained all remaining papers for evaluation of their potential relevance according to the titles and abstracts.The selected papers were evaluated for full-text eligibility, with the aim of collating data on age-specific dengue seroprevalence studies conducted in the general population in

Research in context
Evidence before this study With half of the world's population at risk of infection, dengue poses a significant public health challenge worldwide.However, its transmission intensity in several endemic countries remains poorly understood, making it challenging to assess the burden of disease and the potential impact of control interventions.

Added value of this study
In this work, we conducted a scoping literature review to gain a more comprehensive understanding of the heterogeneity in dengue transmission intensity within endemic countries.We collated global dengue age-specific seroprevalence data from endemic areas published between 2014 and 2023 in Medline, Embase and Web of Science.These data were used to calibrate mathematical models and estimate the average yearly force of infection (FOI), which is a fundamental measure of transmission intensity.

Implications of all the available evidence
We found a total of 66 new publications with 219 relevant datasets from 30 dengue endemic countries and estimated the dengue FOI at the finest available spatial resolution.The FOI estimates published in this study quantify the average risk of infection, characterise its spatial heterogeneities, and can be used to estimate dengue infection and disease burden as well as the potential impact of new interventions, such as vaccination, thus contributing to ongoing efforts to better characterise and map dengue transmission intensity and burden worldwide.specific, geolocated endemic settings.Following Brady et al., 16 we defined endemic countries as those with "good" or "complete presence" of dengue.
Given our focus on estimating the susceptibility profile of the population and the FOI, we excluded papers where only the overall seroprevalence (i.e., not stratified by age) was reported, or where the study focused on hospital cases, clinical trials of antivirals, vaccine studies, or reported a secondary analysis of data published elsewhere (in which case, we included the original article presenting the primary data when the publication date fell within the search criteria).From the selected papers, we recorded the country and specific location of the survey (when available), the age range of the subjects, the number of age groups tested and the respective survey sizes, as well as the date of the survey and the type of assay used.

Estimating the force of infection
We used catalytic models and a Bayesian inferential approach to estimate the dengue force of infection λ from serological data, as done by Imai et al. 4 We used a simple catalytic model (model A), which assumes a constant infection hazard λ and no waning of immunity, to fit most cross-sectional serological (IgG) datasets, with the proportion seropositive subjects in age group a i (z(a i )) given by Eq (1), where i denotes the age group and a i the mid-age of the age group.Whenever the data showed evidence of declining seroprevalence with age, we used model B, which is a modified version of model A with an extra parameter α, representing the antibody decay rate to account for antibody waning.Assuming a constant force of infection λ and decay rate α, the seroprevalence for model B is given by Eq (2).
We fitted two versions of models A and B to the agestratified serological data, assuming a binomial (models A1 and B1) and beta-binomial (models A2 and B2) distribution of the observed age-stratified seroprevalence, using the Hamiltonian Monte-Carlo algorithm implemented in the CmdStanR. 17,18n models A2 and B2, we accounted for overdispersion of the data through an over-dispersion parameter γ, which was separately estimated for each spatial location as described by Imai et al. 4 For the datasets where we only had information on the percentage of people testing positive, and not on the total number of tested people, we fitted the data using model C, where we assumed that the age-stratified seroprevalence (X a i ) was normally distributed with mean (μ a i = z (a i )) and variance (σ 2 ) to be estimated, as described by Eq (3).
For the analysis of PRNT (Plaque Reduction Neutralization Test) results, since we only had information on the percentage of seropositive subjects per serotype, we used a modified version of model C, named model D, where we assumed a serotype-specific average FOI λ j and that the mean of the seroprevalence shown in Eq (3) was serotype specific, as shown in Eq. ( 4).
We estimated the average force of infection across the four serotypes λ as the average of the serotypespecific mean FOIs λ j .We assumed informative prior distributions on all estimated parameters.The prior distributions of the FOI λ and antibody decay rate α were defined as normal distribution with mean 0.1 and standard deviation 0.1, while for the variance σ 2 , we assumed a normal prior distribution with mean 0 and standard deviation 1, and for the over-dispersion parameter γ we assumed an exponential prior distribution centred in 1.We set a lower bound of 0 for all parameters.
For each dataset we reported the average age of first infection, calculated as 1/λ, and the age at which we expect x seroprevalence (with x = 50% and 70%), calculated as −ln(1−x)/λ for model A, C and D and as −ln(1−x λ+α λ ) /(λ +α) for model B.

Statistics
We used Rhatt, n eff and visual inspection of the trace plots to assess convergence. 17The deviance information criterion (DIC) was used for model selection.The DIC is defined as DIC = 2ll(θ) − 4ll(θ), where and ll(θ) and ll(θ) respectively denote the loglikelihood computed at the mean of the parameters and the mean loglikelihood.Models with the lowest DIC are preferred, and differences in DIC of at least 4 units are typically used for model selection. 19We calculated the median and 95% credible interval (CrI) for each estimated parameter.Uncertainty around the modelled seroprevalence, the average age of first infection and the age of 50% and 70% seroprevalence were estimated by extracting 100 random values from the posterior distribution of the parameters and reporting the median and 95% CrI (2.5 and 97.5 percentiles) of the modelled statistic.Each observed seroprevalence data point was presented as mean and the 95% binomial confidence interval (CI) calculated with the Agresti-Coull method. 20

Role of funders
The funders had no role in the study design, data collection, data analyses, interpretation or writing of report; the findings and conclusions contained herein are those of the authors and do not necessarily reflect positions or policies of the aforementioned funding bodies.

Article selection and dataset characteristics
We found a total of 5690 potentially relevant articles.
Once we removed duplicates, 4633 papers were retained for evaluation of titles and abstracts.Of these, 4404 articles were found to be irrelevant for the purpose of the study and were therefore excluded.The remaining 229 papers were evaluated for full-text eligibility, from which we identified 66 studies reporting age-specific seroprevalence datasets (Fig. 1) from surveys conducted between 2006 and 2023 in various dengue endemic countries and published after 2014 (Fig. 2).This literature review identified 219 new dengue FOIs from serological survey, more than five times the number published in Imai et al., 4 which indicates an increased interest in using serology for dengue surveillance worldwide in the last decade (Fig. 2).
Table 1 reports the estimated yearly FOI together with the main characteristics of each serosurvey, including the country, location, age range, number of age groups tested and size of the survey, the type of assay used and relative commercial name, and the date when the survey was performed.IgG ELISA was the assay most frequently used.Out of 66 studies, 17 focused on children and young people (up to 22 years old), mainly tested in schools and universities.

Force of infection estimates
Table 1 and Fig. 3 summarise the FOI estimates obtained for each location and country under the best fitting model, which was selected according to the DIC metric.The FOI estimates and DIC values obtained with each model version are presented in the Supplementary data (Fig. S8 and Tables S3 and S4).
As shown in Fig. 3, this study generated several FOI estimates from Indonesia, Madagascar, Mexico and India.
The 66 papers selected in this study generated a total of 219 dengue FOI estimates which were most frequently obtained using model A (Table 1).For the four datasets (from Kenya, 53 Brazil, 24 French Guiana, 35 French Polynesia 36 ) reporting age-specific positivity rates (not the number of positive and tested subjects) the FOI was estimated with model C, while for the two papers reporting serotype-specific seroprevalence estimates using the PRNT assay (in Peru 68 ) and the Hemagglutination inhibition (HI) assay (in Laos 55 ), we used model D to estimate the overall FOI.
Overall, Laos, Haiti, India, Indonesia and Mexico showed the highest yearly FOI estimates, with average values around 0.2, which correspond to an expected average age of first infection of around 5 years.On the other hand, Taiwan was the country with the lowest estimated yearly FOI, with an average estimate around 0.0011 (95% CrI 0.0011, 0.0013).
Fig. 4 shows the geographical distribution of the dengue serosurveys identified in this analysis and the resulting FOI estimates.We find evidence of a high risk of infection across India, Indonesia, Laos and Haiti, with the highest FOI estimate being estimated in Kendari (Indonesia), with a median FOI of 0.34 (95% CrI 0.25-0.45).Lower transmission intensities were found in countries such as Taiwan (median FOI between 0.001 and 0.005) and Saudi Arabia (median FOI between 0.008 and 0.017), which are located on the outskirts of tropical and subtropical regions (Fig. 4).Interestingly, we estimated among the lowest FOI estimates in the North-east of India (median FOI of 0.002) and in Laos (median FOI of 0.001), which are also the countries with the highest dengue FOI estimates-this highlights the vast differences in the intensity of dengue transmission within these two countries.
The patterns of dengue transmission in Latin America and the Caribbean are heterogeneous, with Haiti showing a high FOI estimate of 0.209 (95% CrI 0.100-0.352),while some locations in Brazil (e.g., the state of Ceara) and Mexico (e.g., Salamanca) showing the lowest FOI in these areas (with a mean of 0.009 (95% CrI 0.007, 0.011) and 0.001 (95% CrI 0.00, 0.007), respectively).

Discussion
Age-stratified serological surveys represent an ideal surveillance tool to estimate the risk of dengue infection and to reconstruct the age-specific susceptibility profile and historical circulation of dengue in the surveyed populations. 54In this study, 66 publications were retrieved from published databases, which provided 219 age-stratified dengue seroprevalence datasets collected between 2006 and 2023 from 30 different countries including 38 from Africa, 97 from Asia, 72 from North America, 1 from Oceania, and 11 from South America.Compared to the database collated in Imai et al., 4 we found five times more FOI estimates from studies published since 2014, demonstrating an increasing number of dengue serosurveys being conducted globally in the last decade.
We found that the FOI estimates obtained with models A and B (which assumed antibodies did (B) or did not (A) wane over time) were consistent (Fig. S13).As expected, we found high heterogeneities in the average FOI within and between countries. 3The newly identified age-stratified seroprevalence datasets from the Ratchaburi province of Thailand and Singapore generated FOI estimates that are consistent with the estimates obtained from previous studies. 4On the other hand, the FOI estimates obtained in this study for Lahore (Pakistan) and Colombo city (Sri Lanka) are higher than those previously published in Imai et al., 4 highlighting changes in FOI, potentially linked with the geographical expansion of dengue circulation, 41 and increases in urbanization and population density. 45As for the other countries previously studied in Imai et al. 4 -Brazil, Laos, India, Indonesia, Haiti and Mexico -we identified new age-stratified serosurveys in different locations from those previously identified, which also gave higher FOI estimates than previously recorded.Serosurveys performed in Peru, Vietnam and French Polynesia gave lower FOI estimates than those reported in Imai et al., 4 which is consistent with the highly heterogeneous nature of dengue transmission (given these were performed in different locations compared to those identified in Imai et al. 4 ).
This study also identified countries where agestratified dengue seroprevalence surveys were conducted for the first time since 2014.These include several endemic countries in Central and South America (Ecuador, Guadeloupe, Martinique, Colombia and Venezuela), as well as in Asia (Malaysia, Bangladesh, Taiwan, French Guiana and Saudi Arabia), and Africa (Burkina Faso, Tanzania, Kenya, Cameroon, Gabon, Djibouti, Nigeria and Madagascar).Of these, the ones from Ecuador, Guadeloupe, Martinique, French Guiana, Gabon, Djibouti, Madagascar and Burkina Faso are the first to be published in the respective countries. 15In line with our current understanding of global trends of dengue transmission, we find the highest FOI in Indonesia, India, Laos and Haiti, while the lowest in Taiwan and Saudi Arabia.
Through our literature review, we found two agestratified dengue seroprevalence surveys conducted in Bangladesh, one in Dhaka city conducted in 2012 22 and one conducted at the national level in 2022. 21These datasets report rather different age-dependent immunity profiles, with the national survey (performed 10 years after the one in Dhaka city) reporting a higher FOI estimate.Whilst the seroprevalence survey conducted in Dhaka city was a household serosurvey and included all age groups, the national survey was conducted in blood donors (and did not include individuals <18 years); differences in the surveyed populations could potentially explain the observed differences in the age-stratified seroprevalence profile, but overall this result suggests an increase in dengue transmission intensity across the country over the last 10 years.
Before this study, 13 published age-stratified seroprevalence surveys from Africa, specifically from Cameroon, Namibia, Nigeria, Kenya, Sudan and Tanzania had been identified. 15This review identified 8 additional age-stratified seroprevalence datasets from the African Region: three national surveys in Kenya, Djibouti (Horn of Africa) and Madagascar, two local surveys in Ougadougou (Burkina Faso), one in Osogbdo (Nigeria) and one in Zanzibar (Tanzania), three townlevel serosurveys in the towns of Doualaa and Garoua (Cameroon) and Lambarene (Gabon), and a multiregional survey in Tanzania (Buhigwe, Kalambo, Kilindi, Kinondoni, Kondoa, Kyela, Mvomero and Ukerewe).Amongst all the FOI estimates obtained across Africa, the serosurvey conducted in 2010 in Tanzania shows the highest FOI estimate (median 0.114 (95% CrI 0.034-0.254)),consistent with previous estimates obtained from blood donors. 81tably, we found two age-stratified dengue seroprevalence surveys from Tanzania, one at a multiregional level 80 and one in Zanzibar, 81 reporting rather different age-dependent susceptibility profiles.The multi-regional survey performed in 2018 suggests a relatively recent introduction of dengue (flat immunity profile), whilst the results reported in Zanzibar in 2010 show a clearly increasing age-dependent seroprevalence profile.These differences could reflect fundamental differences in transmission intensity, given that Aedes aegypti mosquitoes are urban vectors, and hence dengue is expected to circulate at higher intensity in urban settings, whilst the multi-regional seroprevalence profile likely captures the average immunity profile of the population at the national level, including in rural settings where dengue is likely to be circulating at lower intensities.
This study has a number of limitations, including the choice of relying on the Embase, Medline and Web of Science databases and the inclusion of papers written in the English language only, consistent with the criteria used in Imai et al. 4 We also assumed time-and ageconstant FOIs (and reporting rates), which capture long-term average transmission intensities that can be directly compared with the constant-in-time FOI maps generated in Cattarino et al., 15 which we are planning to update with the estimates generated in this literature review.Moreover, in this analysis we did not account for the different sensitivities and specificities of the different tests used across the serosurveys, as the inhouse accuracy of the assays is rarely reported in Fig. 2: Location and year of published dengue seroprevalence studies.Panel A: location of published age-stratified dengue seroprevalence data, as identified in Imai et al. 4 (green) and in this literature review (purple).Panel B: number of age-stratified dengue serosurveys per year as identified in Imai et al. 4 (green) and in this literature review (purple) by year of the serosurvey.Panel C: number of age-stratified dengue serosurveys by serosurvey year and continent (in colour).The estimated force of infection (FOI) is expressed in years and reported as median and 95% credible intervals (CrI).We used * when the date of the survey was not specified, and in these instances we report the year of publication.We used + when the upper bound of the age group was not specified.
Table 1: Summary of age-specific seroprevalence datasets identified in this literature review and location-specific yearly FOI estimates provided as median and 95% CrI.
use of serological data as these data do not depend on the sensitivity of surveillance.This allows for withinand cross-country comparisons that can be used to investigate the fundamental environmental, demographic and climatic drivers of transmission underlying the observed heterogeneities.In this work, we have also tested the effect of alternative modelling assumptions on the FOI estimates, which is important to inform global comparisons of dengue transmission and burden.
In future work, it will be important to validate the serological-derived FOIs against those derived from age-stratified case-notification data, given the latter are collated in the countries where dengue is a notifiable disease (though these may not always be open access).Assessing the evidence of age-trends in case reporting and the model's ability to reconstruct agedependencies is key to validating the use of casenotification data for estimating the FOI, which further strengthens the value of the serological data collated in this study.
In summary, the dengue FOI estimates generated in this study, along with the estimates obtained in previous analyses 15 (Table S11), provide an up to date compendium of global FOI estimates obtained from age-stratified seroprevalence surveys, which will be useful to update the existing dengue burden estimates and to evaluate the impact of interventions to inform public health policy locally and globally.

Conclusion
Age-stratified serological surveys provide key epidemiological data for estimating the transmission intensity of dengue, which in turn can be used to refine burden estimates and inform the optimal implementation of interventions such as vaccination.Our findings, alongside the serological data and FOI estimates published in Imai et al. 4 and Cattarino et al., 15 summarize our current understanding of global heterogeneities in transmission intensity, as well as disparities in the geographical distribution of agestratified serological surveys, thus highlighting regional gaps in arbovirus surveillance, which can help inform current and future efforts to strengthen surveillance capacity locally and globally.access to a higher-resolution version of the dengue age-stratified seroprevalence datasets published in Madagascar and Mexico.This contribution has been invaluable to generate dengue FOI estimated at finer spatial resolution compared to the published data.1.

Fig. 1 :
Fig. 1: PRISMA chart of the literature search.Literature review on dengue age-stratified seroprevalence studies published from 2014 to the end of October 2023 in Embase, Medline and Web of Science using the word search "dengue AND sero* AND (prevalence OR seroprevalence OR positiv* OR seropositiv*).

Fig. 3 :
Fig. 3: FOI estimates obtained in this study, grouped by country and using the best fitting model.The x-axis shows the year of the serological survey and the y-axis shows the median (point) and 95% CrI (line) of the FOI estimates.Different locations within the same country are presented in different colours.
CM and ID acknowledge funding from the Drugs for Neglected Diseases initiative (DNDi) and the MRC Centre for Global Infectious Disease Analysis (reference MR/X020258/1), funded by the UK Medical Research Council (MRC).This UK funded award is carried out in the frame of the Global Health EDCTP3 Joint Undertaking; and funding by Community Jameel.ID acknowledges research funding from a Sir Henry Dale Fellowship funded by Wellcome Trust (grant 213494/Z/18/Z).DNDi thanks the French Development Agency (AFD), France; Médecins Sans Frontières International; Swiss Agency for Development and Cooperation (SDC), Switzerland; UK aid, UK; for the financial support in this work.Funding: UK Medical Research Council, Wellcome Trust, Community Jameel, Drugs for Neglected Diseases initiative (DNDi) funded

Fig. 4 :
Fig.4: Worldwide distribution and geolocation of the average force of infection (FOI) estimates obtained in this study.Average FOI estimates obtained using the best model according to the DIC criterion, as summarized in Table1.